Prediction of Landslide Displacement Based on the Combined VMD-Stacked LSTM-TAR Model

Abstract

The volatility of the cumulative displacement of landslides is related to the influence of external factors. To improve the prediction of nonlinear changes in landslide displacement caused by external influences, a new combined forecasting model of landslide displacement has been proposed. Variational modal decomposition (VMD) was used to obtain the trend and fluctuation sequences of the original sequence of landslide displacement. First, we established a stacked long short time memory (LSTM) network model and introduced rainfall and reservoir water levels as influencing factors to predict the fluctuation sequence; next, we used a threshold autoregressive (TAR) model to predict the trend sequence, following which the trend and fluctuation prediction sequence were superimposed to obtain the cumulative predicted displacement of the landslide. Finally, the VMD-stacked LSTM-TAR combination model based on the variational modal decomposition, stacked long short time memory network, and a threshold autoregressive model was built. Taking the landslide of Baishuihe in the Three Gorges Reservoir area as an example, through comparison with the prediction results of the VMD-recurrent neural network-TAR, VMD-back propagation neural network-TAR, and VMD-LSTM-TAR, the proposed combined prediction model was noted to have high accuracy, and it provided a novel approach for the prediction of volatile landslide displacement.

Landslide displacement prediction based on Variational Mode Decomposition and Temporal Convolutional Network

“…The results are shown in Figure 12. In Table 4, we include the results of Gao et al [19] and Wang et al [17] for comparison. Although some predicted values deviated slightly from the actual measurements, the VMD-TCN model still exhibited the best performance.…”

“…VMD currently has many applications for mechanical fault diagnosis [27] and has been applied for landslide displacement prediction [19]. The VMD algorithm decomposes complex signals with non-smooth, nonlinear sequences into multiple intrinsic mode functions (IMFs) [21].…”

“…Data-driven models use algorithms to find correlations and patterns between landslide displacement data and impact factors, which have high prediction accuracy and low implementation cost. The main data-driven models are autoregressive (AR) models [11], regression models [11], random forests(RF) [12], extreme learning machine (ELM) [13], support regression vector machine (SVM) [14,15], BP neural network [16], and LSTM models [17][18][19][20].…”

Prediction of Deformation Caused by Landslides Based on Graph Convolution Networks Algorithm and Dinsar Technique

“…Frequently, landslides result in the destruction of the structure and infrastructure of villages and towns, creating a danger to residents as well as causing significant property damage (Miele, 2021), (Del Soldato, 2019). A great deal of attention has been paid to monitoring and predicting disasters by industry and academic institutions (Bozzano, 2011), (Gao, 2022). The city of Moio della Civitella (Salerno Province) is among the sites with the greatest concentration of landslides in the world, which damaged its urban settlement (Infante, 2019), (Di Martire, 2015).…”

Geomorphological transformations and future deformation estimations of a large potential landslide in the high-order position area of Diexi, China

“…In particular, machine learning models exhibit a better performance than other mathematical models, and the representation of different machine learning models is variant 33 . In addition, the majority have a strong reliability, particularly improved models that perform better 35 . Some organizations require the monitoring and prediction of multiple landslides, thus early warning systems have been developed 36 .…”